(untitled) (REMUNOM, 2026)
This article presents a prototype proposal for multimodal patent similarity analysis, integrating textual and visual information to enhance search capabilities and support prior art analysis. The methodology employs TF-IDF for generating textual embeddings and a simplified approach for visual embeddings, based on image resizing, grayscale conversion, and flattening. The implementation was carried out in a Python environment, with direct data ingestion from patent links obtained from Google Patents, enabling automated execution of extraction and analysis processes. The generated embeddings are combined through a weighted multimodal scoring function, allowing the identification of similar patents. Additionally, a basic in-memory knowledge graph is constructed to represent relationships among the analyzed documents. The results demonstrate the feasibility and benefits of integrating multiple data modalities, providing an initial system for improving patent retrieval. Although based on simplified techniques, this work establishes a foundation for future advancements, including the adoption of more sophisticated embedding models, scalable vector search solutions, and persistent knowledge graph databases.